Surveys in Geophysics

, Volume 38, Issue 6, pp 1425–1443 | Cite as

Airborne Lidar Observations of Water Vapor Variability in Tropical Shallow Convective Environment

  • Christoph Kiemle
  • Silke Groß
  • Martin Wirth
  • Luca Bugliaro


An airborne downward-pointing water vapor lidar provides two-dimensional, simultaneous curtains of atmospheric backscatter and humidity along the flight track with high accuracy and spatial resolution. In order to improve the knowledge on the coupling between clouds, circulation and climate in the trade wind region, the DLR (Deutsches Zentrum für Luft- und Raumfahrt) water vapor lidar was operated on board the German research aircraft HALO during the NARVAL (Next Generation Aircraft Remote Sensing for Validation Studies) field experiment in December 2013. Out of the wealth of about 30 flight hours or 25,000 km of data over the Tropical Atlantic Ocean east of Barbados, three ~ 2-h-long, representative segments from different flights were selected. Analyses of Meteosat Second Generation images and dropsondes complement this case study. All observations indicate a high heterogeneity of the humidity in the lowest 4 km of the tropical troposphere, as well as of the depth of the cloud (1–2 km thick) and sub-cloud layer (~ 1 km thick). At the winter trade inversion with its strong humidity jump of up to 9 g/kg in water vapor mixing ratio, the mixing ratio variance can attain 9 (g/kg)2, while below it typically ranges between 1 and 3 (g/kg)2. Layer depths and partial water vapor columns within the layers vary by up to a factor of 2. This affects the total tropospheric water vapor column, amounting on average to 28 kg/m2, by up to 10 kg/m2 or 36%. The dominant scale of the variability is given by the extent of regions with higher-than-average humidity and lies between 300 and 600 km. The variability mainly stems from the alternation between dry regions and moisture lifted by convection. Occasionally, up to 100-km large dry regions are observed. In between, convection pushes the trade inversion upward, sharpening the vertical moisture gradient that is colocated with the trade inversion. In most of the water vapor profiles, this gradient is stronger than the one located at the top of the sub-cloud layer. Lidar observations in concert with models accurately reproducing the observed variability are expected to help evaluate the role these findings play for climate.


Airborne lidar Water vapor lidar Shallow convection Trade wind region Cloud layer 



This paper arises from the International Space Science Institute (ISSI) workshop on “Shallow clouds and water vapor, circulation and climate sensitivity.” Valuable support during the flight campaign was provided by Andreas Fix, Christian Büdenbender and Axel Amediek, all DLR. The NARVAL campaign was co-sponsored by the Max Planck Society, the Deutsche Forschungsgemeinschaft (German Science Foundation, project HALO-SPP 1294) and the DLR Institute of Atmospheric Physics. The dropsonde data were processed by Yanfei Gong, DLR, who also tested layer separation methods. We are grateful to Klaus Gierens, DLR, who provided an internal review, to Andreas Schäfler, DLR, for helpful discussions, and to an anonymous reviewer for many valuable comments.


  1. Behrendt A, Wulfmeyer V, Kiemle C, Ehret G, Flamant C, Schaberl T, Bauer H-S, Kooi S, Ismail S, Ferrare R, Browell EV, Whiteman DN (2007) Intercomparison of water vapor data measured with lidar during IHOP_2002 Part II airborne-to-airborne systems. J Atmos Ocean Technol 24:22–39CrossRefGoogle Scholar
  2. Bhawar R et al (2011) The water vapour intercomparison effort in the framework of the convective and orographically-induced precipitation study: airborne-to-ground-based and airborne-to-airborne lidar systems. Q J R Meteorol Soc 137:325–348. doi: 10.1002/qj.697 CrossRefGoogle Scholar
  3. Bielli S, Grzeschik M, Richard E, Flamant C, Champollion C, Kiemle C, Dorninger M, Brousseau P (2012) Assimilation of water-vapour airborne lidar observations: impact study on the COPS precipitation forecasts. Q J R Meteorol Soc. doi: 10.1002/qj.1864 Google Scholar
  4. Bony S, Stevens B, Frierson D et al (2015) Clouds, circulation, and climate sensitivity. Nat Geosci 8:261–268. doi: 10.1038/ngeo2398 CrossRefGoogle Scholar
  5. Bony S, Stevens B, Ament F et al (2017) EUREC4A: a field campaign to elucidate the couplings between clouds, convection and circulation. Surv Geophys. doi: 10.1007/s10712-017-9428-0 Google Scholar
  6. Di Girolamo P, Behrendt A, Kiemle C, Wulfmeyer V, Bauer H, Summa D, Dörnbrack A, Ehret G (2008) Simulation of satellite water vapour lidar measurements: performance assessment under real atmospheric conditions. Remote Sens Environ 112(4):1552–1568CrossRefGoogle Scholar
  7. Fischer L, Craig GC, Kiemle C (2013) Horizontal structure function and vertical correlation analysis of mesoscale water vapor variability observed by airborne lidar. J Geophys Res 118:1–12. doi: 10.1002/jgrd.50588 Google Scholar
  8. Flentje H, Dörnbrack A, Ehret G, Fix A, Kiemle C, Poberaj G, Wirth M (2005) Water vapour heterogeneity related to tropopause folds over the North Atlantic revealed by airborne water vapour differential absorption lidar. J Geophys Res 110:D03115. doi: 10.1029/2004JD004957 CrossRefGoogle Scholar
  9. Groß S, Wirth M, Schäfler A, Fix A, Kaufmann S, Voigt C (2014) Potential of airborne lidar measurements for cirrus cloud studies. Atmos Meas Technol 7:2745–2755CrossRefGoogle Scholar
  10. Gutleben M, Groß S, Wirth M, Ewald F, Schäfler A (2017) Applicability of spaceborne lidar measurements to study shallow marine convection over the subtropical North Atlantic Ocean. To be submitted to AMTGoogle Scholar
  11. Kiemle C, Ehret G, Giez A, Davis KJ, Lenschow DH, Oncley SP (1997) Estimation of boundary-layer humidity fluxes and statistics from airborne differential absorption lidar (DIAL). J Geophys Res 102(D24):29189–29203CrossRefGoogle Scholar
  12. Kiemle C, Brewer WA, Ehret G, Hardesty RM, Fix A, Senff C, Wirth M, Poberaj G, LeMone MA (2007) Latent heat flux profiles from collocated airborne water vapor and wind lidars during IHOP_2002. J Atmos Ocean Technol 24:627–639. doi: 10.1175/JTECH1997.1 CrossRefGoogle Scholar
  13. Kiemle C, Wirth M, Fix A, Ehret G, Schumann U, Gardiner T, Schiller C, Sitnikov N, Stiller G (2008) First airborne water vapor lidar measurements in the tropical upper troposphere and mid-latitudes lower stratosphere: accuracy evaluation and intercomparisons with other instruments. Atmos Chem Phys 8:5245–5261CrossRefGoogle Scholar
  14. Kiemle C, Wirth M, Fix A, Rahm S, Corsmeier U, Di Girolamo P (2011) Latent heat flux measurements over complex terrain by airborne water vapour and wind lidars. Q J R Meteorol Soc 137:190–203. doi: 10.1002/qj.757 CrossRefGoogle Scholar
  15. LeMone MA, Pennell WT (1976) The relationship of trade wind cumulus distribution to subcloud layer fluxes and structure. Mon Weather Rev 104:524–539CrossRefGoogle Scholar
  16. Mech M, Orlandi E, Crewell S, Ament F, Hirsch L, Hagen M, Peters G, Stevens B (2014) HAMP—the microwave package on the high altitude and long range research aircraft (HALO). Atmos Meas Tech 7:4539–4553. doi: 10.5194/amt-7-4539-2014 CrossRefGoogle Scholar
  17. Naumann A-K, Stevens B, Hohenegger C, Mellado JP (2017) A conceptual model of a shallow circulation induced by prescribed low-level radiative cooling. J Atmos Sci. doi: 10.1175/JAS-D-17-0030.1 Google Scholar
  18. Nehrir AR, Kiemle C, Lebsock M, Kirchengast G, Buehler SA, Löhnert U, Liu CL, Hargrave P, BarreraVerdejo M, Winker D (2017) Emerging technologies and synergies for airborne and space-based measurements of water vapor profiles. Surv Geophys (in press)Google Scholar
  19. Nuijens L, Medeiros B, Sandu I, Ahlgrimm M (2015) Observed and modeled patterns of covariability between low-level cloudiness and the structure of the trade-wind layer. J Adv Model Earth Syst. doi: 10.1002/2015MS000483 Google Scholar
  20. Pincus RA, Beljaars A, Kirchengast G, Buehler BS, Landstaedter F (2017) The distribution of water vapor over low-latitude oceans: current best estimates, errors, and impacts. Surv Geophys (in press)Google Scholar
  21. Prospero JM, Collard F-X, Molinié J, Jeannot A (2014) Characterizing the annual cycle of African dust transport to the Caribbean Basin and South America and its impact on the environment and air quality. Glob Biogeochem Cycles 29:757–773. doi: 10.1002/2013GB004802 CrossRefGoogle Scholar
  22. Randel WJ, Rivoire L, Pan LL, Honomichl SB (2016) Dry layers in the tropical troposphere observed during CONTRAST and global behavior from GFS analyses. J Geophys Res Atmos 121:14142–14158. doi: 10.1002/2016JD025841 CrossRefGoogle Scholar
  23. Schäfler A, Dörnbrack A, Kiemle C, Rahm S, Wirth M (2010) Tropospheric water vapor transport as determined from airborne lidar measurements. J Atmos Ocean Technol 27:2017–2030. doi: 10.1175/2010JTECHA1418.1 CrossRefGoogle Scholar
  24. Schmetz J, Pili P, Tjemkes S, Just D, Kerkmann J, Rota S, Ratier A (2002) An introduction to meteosat second generation (MSG). Bull Am Meteorol Soc 83:977–992. doi: 10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2 CrossRefGoogle Scholar
  25. Sherwood SC, Bony S, Dufresne J-L (2014) Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505:37–42CrossRefGoogle Scholar
  26. Stevens B (2005) Atmospheric moist convection. Annu Rev Earth Planet Sci 33:605–643. doi: 10.1146/ CrossRefGoogle Scholar
  27. Stevens B, Brogniez H, Kiemle C, Lacour J-L, Crevoisier C, Killiani J (2017) Structure and dynamical influence of water vapor in the lower tropical troposphere. Surv Geophys. doi: 10.1007/s10712-017-9420-8 Google Scholar
  28. Stull R (2015) Practical meteorology: an algebra-based survey of atmospheric science. University of British Columbia, p 938.
  29. Trickl T et al (2016) How stratospheric are deep stratospheric intrusions? LUAMI 2008. Atmos Chem Phys 16:8791–8815. doi: 10.5194/acp-16-8791-2016
  30. Wirth M, Fix A, Mahnke P, Schwarzer H, Schrandt F, Ehret G (2009) The airborne multi-wavelength water vapor differential absorption lidar WALES: system design and performance. Appl Phys B 96:201–213CrossRefGoogle Scholar
  31. Wulfmeyer V, Hardesty RM, Turner DD, Behrendt A, Cadeddu MP, Di Girolamo P, Schlüssel P, Van Baelen J, Zus F (2015) A review of the remote sensing of lower tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles. Rev Geophys 53:819–895. doi: 10.1002/2014RG000476 CrossRefGoogle Scholar
  32. Zuidema P, Torri G (2017) Precipitation-induced oceanic cold pools and their interactions with the larger-scale environment. Surv Geophys (in press)Google Scholar
  33. Zuidema P et al (2012) On trade wind cumulus cold pools. J Atmos Sci 69:258–280. doi: 10.1175/JAS-D-11-0143.1 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.DLR, Deutsches Zentrum für Luft- und RaumfahrtInstitut für Physik der AtmosphäreOberpfaffenhofenGermany

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